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Open-Source MCP servers

Production-ready MCP servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

9,448 servers. Last updated -

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Matching MCP servers:

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    An advanced MCP server providing RAG-enabled memory through a knowledge graph with vector search capabilities, enabling intelligent information storage, semantic retrieval, and document processing.
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    An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.
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    Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.
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    Apache 2.0
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    "primitive" RAG-like web search model context protocol server that runs locally. ✨ no APIs ✨
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    Implements a RAG workflow that integrates with any custom knowledge base and can be triggered directly from the Cursor IDE.
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    An MCP server that provides comprehensive multimodal Retrieval-Augmented Generation (RAG) capabilities for processing and querying document directories, supporting text, images, tables, and equations.
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    MIT License

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    An MCP server that enables RAG (Retrieval-Augmented Generation) on markdown documents by converting them to embedding vectors and performing vector search using DuckDB.
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    Apache 2.0
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    A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.
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    Apache 2.0
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    Provides AI agents and coding assistants with web crawling and RAG capabilities, allowing them to scrape websites and perform semantic searches on the crawled content.
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    A TypeScript MCP server that allows querying documents using LLMs with context from locally stored repositories and text files through a RAG (Retrieval-Augmented Generation) system.
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    Implements Retrieval-Augmented Generation (RAG) using GroundX and OpenAI, allowing users to ingest documents and perform semantic searches with advanced context handling through Modern Context Processing (MCP).
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    Web crawling and RAG implementation that enables AI agents to scrape websites and perform semantic search over the crawled content, storing everything in Supabase for persistent knowledge retrieval.
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    MIT License
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    An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.
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    Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.
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    Connects a RAG application to open-webui using Model Context Protocol (MCP), enabling server-to-client communication for context retrieval and tool usage in remote environments through Server-Sent Events (SSE).
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    Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.
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    Transform your AI agents into Apple development experts! Apple RAG MCP gives you instant access to official Swift docs, design guidelines, and comprehensive Apple platform knowledge through cutting-edge RAG technology. With professional AI reranking and hybrid search across all official Apple docs.
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    A Model Context Protocol server that exposes Retrieval-Augmented Generation capabilities and a weather tool, allowing clients to interact with document knowledge bases and retrieve weather information.
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    Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.
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    MIT License
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    An agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.
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    A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.
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    An MCP server that provides detailed information about your development environment to the Cursor code editor, enabling more context-aware assistance.
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    MIT License
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    Provides AI agents and coding assistants with advanced web crawling and RAG capabilities, allowing them to scrape websites and leverage that knowledge through various retrieval strategies.
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    Enables beginner-friendly Python and Pybricks development support through RAG-powered tools that search official documentation, suggest code snippets, and provide version-aware guidance for LEGO robotics programming.
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    Connects Bear Notes to AI assistants using semantic search and RAG (Retrieval-Augmented Generation), allowing AI systems to access and understand your personal knowledge base through meaningful search rather than just keyword matching.
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    A server exposing intelligent tools for enhancing RAG applications with entity extraction, query refinement, and relevance checking capabilities.
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    A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.
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    Provides AI agents with complete web search, crawling, and RAG capabilities through a Docker-based solution combining Model Context Protocol, Crawl4AI, SearXNG, and Supabase.
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    Enables natural language search and interaction with video content through three tools: ingesting videos to a Ragie index, retrieving relevant video segments based on queries, and creating video chunks from specific timestamps.
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    An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.
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    MIT License
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    Provides Retrieval-Augmented Generation capabilities by parsing documents (PDF, Markdown) and storing them in ChromaDB for semantic search and context retrieval.
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    MIT License